N.C. Basantia, Leo M.L. Nollet, Mohammed Kamruzzaman
December 5, 2018 Forthcoming
Reference - 284 Pages - 39 Color & 37 B/W Illustrations
ISBN 9781138630796 - CAT# K32068
Series: Food Analysis & Properties
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In processing food, hyperspectral imaging, combined with intelligent software, enables digital sorters (or optical sorters) to identify and remove defects and foreign material that are invisible to traditional camera and laser sorters. Hyperspectral Imaging Analysis and Applications for Food Quality explores the theoretical and practical issues associated with the development, analysis, and application of essential image processing algorithms in order to exploit hyperspectral imaging for food quality evaluations. It outlines strategies and essential image processing routines that are necessary for making the appropriate decision during detection, classification, identification, quantification, and/or prediction processes.
The book is divided into three sections. Section I discusses the fundamentals of Imaging Systems: How can hyperspectral image cube acquisition be optimized? Also, two chapters deal with image segmentation, data extraction, and treatment. Seven chapters comprise Section II, which deals with Chemometrics. One explains the fundamentals of multivariate analysis and techniques while in six other chapters the reader will find information on and applications of a number of chemometric techniques: principal component analysis, partial least squares analysis, linear discriminant model, support vector machines, decision trees, and artificial neural networks. In the last section, Applications, numerous examples are given of applications of hyperspectral imaging systems in fish, meat, fruits, vegetables, medicinal herbs, dairy products, beverages, and food additives.
Imaging Systems. Fundamentals. Techniques. Image Acquisition, Calibration, Image Pre-processing. Image Segmentation. Data Extraction and Treatment. Detection. Chemometrics. Instrument Grading. Principal Component Analysis. Partial Least Squares Analysis. Fisher's Linear Discriminant Model. Support Vector Machines. Decision Tree. Multivariate Analysis and Techniques. Image Systems: A Part of Non-Invasive Sensing of Food. Image Systems: A Part of Non-Invasive Sensing of Food. Applications. Applications in Fish. Applications in Meat. Applications in Fruits. Applications in Vegetables. Applications in Medicinal Herbs. Applications in Dairy Products.